Healthcare
WISEHealthcareR...
To understand and implement Retrieval-Augmented Generation (RAG) as a key architectural pattern for LLMs to deliver evidence-based, trustworthy Q&A from vast medical literature.
Use Case
Building and querying an AI system that leverages a
proprietary medical knowledge base to provide referenced and highly accurate
answers to clinical and research inquiries.
Core Challenges
Information Overload: Healthcare SMEs struggle to manually review thousands of new papers to stay current.
Slow Decision Support: Traditional search methods are slow and do not provide concise, synthesized answers with source evidence.
Hallucination Risk: Typical / standard LLMs could respond with inaccurate or fabricated information.
Tools & Activities:
The course explores
- How to setup a RAG architecture to explore a medical paper and ground the respond within the information available in a given input (pdf research paper)
- Prompt engineering.
- Interacting with an LLM through the chat interface
- Pinecone vector database configuration, chunking and storage, search and retrieval
- n8n workflow automation
Outcome
Participants will gain the skills to deploy a trustworthy, evidence-based AI system that ensures high factual accuracy and provides instant, referenced answers to complex clinical or research questions.